The next wave of technology to hit the market and takeover the materialist minds of America has a slightly terrifying side. As the Apple Watch hits the market in the next month or so, there will be an influx of data on every person wearing an Apple Watch that Apple has access to. Apple will be able to monitor all of the wearers and have a personal look into all of their lives. This simple fact begs the question, “Is this an invasion of privacy?”
Other devices that are in their infant stages of release and popularity include the Whistle, a dog tracker, and the “Smart Nursery” from Mimo Baby that monitors an infant’s room in order to determine if its healthy, hungry, and happy. As awesome and useful that all of these new devices are, they still make me wonder whether or not they will remove part of the human element from living life. When you don’t have to ever check on your baby, because there’s a computer to do it, will you lose part of the child-parent relationship that so many people cherish today? Will we lose part of our humanity because of the advancement of these technologies?
So as awed and blown away as I am by these astounding technologies, which truly are amazing and seemingly out of the movies, I am utterly terrified of the power which they give to the companies selling them and the government, who could take advantage of them. Also I do not want to slowly lose our grip on what we have left of our humanity due to dependence on computers and gadgets.
Category: Uncategorized
Response
Going through the readers responses to the works, one thing that I was surprised that didn’t come up was the Bacon number. Although one of the readers did mention nodes and connections, nobody talked about the factor that Kevin Bacon is within eight friend layers of every single actor in every single film that the study could find. This is ridiculous when you think of the human race as a network because it shows how close we are truly interconnected.
Another reader wrote about how artificial intelligence is becoming more increasingly life life, there are still short comings. It seems as if my generation is obsessed with imagining a future without those short comings. We see newly released movies like the new Robocop or Chappie. We have corporations like IBM creating some of the most complex AI ever in Watson, the all time leader in Jeopardy. As of today we are only scratching the surface of AI’s, but eventually they will grow into the backbone of our industries, our households, and our lives. One can only speculate about what the future holds for us as a the Human network collides with artificial networks.
Time Between Question and Answer (Part 2)
Unknowingly, I collected the same data as Spencer this week. My data looked similar but a little different.
Tuesday:
Mean (seconds) : 7.07
Standard Deviation (seconds) : 5.46
Thursday:
Mean : 6.28
Standard Deviation : 4.87
The slight alterations in my data vs Spencer’s could be a result of different timing strategies or different questions timed. To explain this data I hypothesized that the more reading we had assigned, the longer it would take between question and answer. Tuesday’s reading assignment was significantly longer than Thursday’s so I thought there would be a large difference between question and answer times. In reality, the means were fairly similar so my hypothesis was likely false. One explanation could be because we don’t always discuss the readings directly. Often we break off and do group work or use our computers. I found that when we are asked to share what we’ve found on the computer, our time after the question is very small, averaging around 2 seconds. This could be because people are excited to share what they’ve found. To get more specific data it could be helpful to categorize the questions into different groups, like questions about the reading or questions about group work, in order to get a better understanding of the time between question and answer.
Observations
Tuesday
Four students were wearing Bean boots
Three students brought coffee
Phones were used nine times while Dr. Sample was talking
Seven people were visibly wearing their misfits
Thursday
Six students were wearing Bean boots
One student brought coffee
Phones were used three times while Dr. Sample was talking
Eight people were visibly wearing their misfits
Another Zoomable Data Visualization
Click on this: http://htwins.net/scale2/
Inaccuracy of Maps
In class the other day we were told to draw maps around campus to other places that we may or may not have known the location. After thinking about the drawings that were made and talking about how different maps could be inaccurate. I wanted to see what different ways in which a map could be inaccurate. So as I was looking on the internet I came across a study done by Colorado University at Boulder looking at the different types of inaccuracies that can be found in maps. The article, “Error, Accuracy, and Precision” talks about “the problems caused by error, inaccuracy, and imprecision in spatial datasets.” Throughout the article it talks about the different inaccuracies. The different types that they bring up are format of the data, age of the data, relevance, density of observations, map scales which all deal with the characteristics of the map itself. It also goes over inaccuracies made through general error such as numerical error and analyzing the data gathered. It was interesting to see the different types of inaccuracies, and it goes with the notion that was brought up in class stating that all maps have errors and are inaccurate.
Questions are constantly brought up about how to make maps more accurate. The article raises some of these questions. How accurate are positional and attribute features? What projection, coordinate system, and datum were used in maps? These are just two of the many questions that can be asked on how to make maps more accurate. What can cartographers do to make sure their maps are as accurate as possible? What are the most important aspects of a map that need to be the most accurate in order for amble data to be taken from it?
XKCD: Map Projections
Regarding Maps and Other Visualizations
In How to Lie With Maps, Mark Monmolier shows us how something we often take for granted, maps, are often designed to purposely persuade us and not to just represent information. Reading through Monmollier’s summary reveals the different ways map data can be construed, and although this deception proves interesting, I personally thought more about data visualization as a whole–and its inherent limitations. Monmollier, for example, discusses how maps must represent either distances or shapes correctly because both cannot be achieved at the same time. The problem stems from trying to represent a 3-dimensional object on a 2-dimensional plane; in the vast majority of cases, loss of information is guaranteed. So, we have to make decisions when mapping data regarding what is important enough to include and what isn’t. This goes, of course, for every attempt to represent data visually, making processes like cartography very interesting. If the point were to be as absolutely accurate as possible, some people probably would not be able to understand what’s going on in the visualization, effectively being shut out. It’s like the oft-quoted difference between OSX and Windows. Make the data too accessible, and parts of it are lost in the simplicity. That’s the trade-off. Mapping data allows us to see the big picture of raw data quickly and clearly without trudging through lines of code, but it misses a lot. So, who decides what data is important and what isn’t? In the case of maps, that’s completely up to the cartographer, who will be hopefully be as truthful as possible; it’s too much of a chore for us to sort through data to make sure it’s all there.
Entering and Leaving Class
This week I wanted to examine how the class filled up by table, how many jackets were on seat-backs, and how the class exited.
On Tuesday, between the snow and some of the sports teams missing we had 20 students, and the picture below shows how many people sat at each table (the number below the square) and the order that in which the tables filled up (the number in the square).
Tuesday:
On Thursday we had a few more people with 22 students. Image follows the same rules as above.
Thursday:
I was also interested to see how many jackets would be placed on seat-backs for the “snow day” vs. non-snow day.
Tuesday: 8 students had jackets on chair-backs.
8/20= 40% of people
Thursday: 12 students had jackets on chair-backs.
12/22= 54.5% of people
The perception of a snow day could have put people in a mind set that told them they were cold and so more people kept their jacket on.
When exiting the room the class has two options: they can leave what I call office side (side facing the front of the library) or writing center side (facing the back of the library).
Tuesday: 16 students left library side.
16/20= 80% of people
Thursday: 13 students left library side.
13/22= 59.1% of people
These numbers could very as people may not have had a class to rush off too on Tuesday, but did on Thursday.
Big Data: positive impact or negative impact?
The argument becomes more and more contentious between customers actively aggregating data profile and being passively collected personal information. The Target’s case put this issue in a more controversial and extreme situation. Since Target’s effort to speculate women’s pregnancy reached a very deep level of personal privacy that most people wouldn’t feel comfortable being asked about by strangers. However, on the other side, while Target is trying to make profit and solicit customers, it is also bring much convenience and promotions to the targeted women customers. Just as many other e-service-based companies like Netflix and Google are doing, most of advertisements or movie recommendations are the result of Big Data analysis. In most cases, we all benefit from the service brought by Big Data technology.
What can also be a backfire other than privacy violation? As I have talked about in the data critique, the minority population might be neglected through the process of Big Data analysis. Every 5 years, American Census Bureau collects data from various aspects like family, work, and education. With the database, the bureau tries to make estimates which can apply to the entire population. However, from the estimates, we can easily recognize the minority groups from the majority. Thus, it is more likely the private companies would design their products for the majority based on the estimates. Is Big Data hurting the minority groups? What should we do to improve? Perhaps we can predict that in the future, Big Data is able to provide the basis for a perfect market which hurts no minorities.
Picture from: Singh, Tarry “Big Data Is The Future Of Digital Marketing” WordPress. July 26th 2014. Web. Feb. 17th 2015. (http://tarrysingh.com/2014/07/big-data-is-the-future-of-digital-marketing/ )